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Research and development of methods for long-term and operational planning of personal affairs using neural networks

Content

Introduction

In today's information society, managing personal affairs is becoming increasingly complex and relevant. Effective planning not only determines the successful achievement of set goals but also influences the overall quality of life. Long-term and operational planning of personal affairs takes on special significance in a rapidly changing world, where the need to adapt to new conditions and make quick decisions becomes crucial.

The use of neural networks, one of the key tools of artificial intelligence, provides new opportunities for more precise, flexible, and efficient personal planning. Neural networks can analyze vast amounts of data, identify patterns, predict future events, and recommend optimal actions.

The relevance of this topic is confirmed by the growing interest in artificial intelligence, machine learning, and neural networks as tools that can improve the quality of life and professional productivity. However, existing methods and approaches to personal planning often have limitations in adapting to specific needs and changing circumstances.

The aim of this research is to develop and explore new methods for long-term and operational personal planning using neural networks, as well as to provide practical recommendations for their implementation. The work will review existing approaches to personal planning and present new methods based on data analysis using neural networks.

The results of this research can have broad practical applications in various fields, from personal time management to business project planning. More effective and adaptive personal planning can significantly improve the quality of life and professional productivity.

1. Relevance of the Topic

The modern world is characterized by fast-paced living, constant changes, and information overload. Optimal management of personal affairs has become a challenge that requires effective strategies and tools. Despite technological advancements and the availability of information, many people feel a shortage of time, struggle with planning their long-term and operational tasks, and face the problem of making informed decisions.

The relevance of the topic "Research and Development of Methods for Long-term and Operational Planning of Personal Affairs Using Neural Networks" is evident from several key factors:

  1. Information Overload: Modern technologies allow us to receive a vast amount of information every day. However, processing this information and applying it in everyday life has become increasingly challenging.
  2. Rapid Changes: The modern world is characterized by rapid changes in the economy, technology, and society. Personal planning needs to be flexible and adaptive to these changes.
  3. Growing Interest in Artificial Intelligence: Neural networks and artificial intelligence are gaining more interest and are being applied in various fields. Their potential for optimizing personal planning has not been fully explored.

Research on this topic is relevant not only from a scientific perspective but also from a practical one. Developing new methods and tools for long-term and operational personal planning using neural networks can significantly improve people's ability to make more informed decisions, optimize the use of time and resources, and enhance their professional and personal effectiveness.

Thus, the relevance of this topic is associated with the increasing need for innovative approaches to managing personal affairs in the modern world. All of this underscores the importance of research aimed at creating intelligent tools for personal task and goal planning.

2. Research Objectives and Expected Outcomes

Research Objective:

The aim of this research is to develop and explore new methods for long-term and operational personal planning using neural networks.

Research Objectives:

To achieve the stated objective, the following tasks need to be addressed:

  1. Analysis of Existing Methods and Approaches to Personal Planning: Conduct a review of existing methods and tools used for personal planning, including time management methods, task prioritization, and decision-making techniques.
  2. Examination of the Applicability of Neural Networks in Personal Planning: Assess the potential and possibilities of using neural networks in the field of long-term and operational personal planning.
  3. Development of New Methods and Tools: Develop new methods and tools based on the use of neural networks for optimizing personal planning. This includes the development of models, algorithms, and interfaces.
  4. Experimental Research and Testing: Conduct experimental research, comparing the developed methods with existing approaches to personal planning to evaluate their effectiveness and applicability.

Expected Outcomes:

The expected outcomes of the research will include:

3. Methodology and Approaches to Personal Planning Using Neural Networks

To achieve the research objective, which is the development and exploration of new methods for long-term and operational personal planning using neural networks, the following methodology and approach will be utilized:

  1. Analysis of Existing Methods and Approaches:

First, an extensive analysis of existing methods and approaches to personal planning will be conducted, including time management methods, task prioritization, and decision-making methods. This stage allows for identifying the advantages and limitations of existing methods and determining areas where the application of neural networks can be most beneficial.

  1. Application of Neural Networks:

At this stage, specific tasks and areas in which neural networks can be applied to optimize personal planning will be identified. This includes selecting types of neural networks, architectures, and training methods that are best suited to address the set tasks.

  1. Development of New Methods and Tools:

During this stage, models and algorithms based on neural networks will be created to address specific personal planning tasks. These methods may involve forecasting task priorities, optimizing time allocation, and automatically suggesting solutions for goal attainment.

  1. Experimental Research and Testing:

After the development of new methods and tools, experimental research will be conducted to compare the results of using the developed methods with the results obtained using existing methods. This will enable the evaluation of the effectiveness and applicability of the new approaches.

The methodology and approaches described above form the basis for conducting research and developing new methods for long-term and operational personal planning using neural networks. This section helps the reader understand how the research will be conducted and which methods will be employed to achieve the objective.

3.1 Study of Existing Methods and Approaches to Personal Planning

There are numerous methods and approaches used in personal planning and personal affairs management. Analyzing these methods is an essential step in identifying the strengths and weaknesses of existing approaches. In this section, an overview of existing methods and approaches is provided:

  1. Time Management: One of the classic methods of personal planning involves time management. This includes the use of calendars, planners, and techniques like the GTD (Getting Things Done) methodology for efficient time utilization and prioritization.[1]
  2. Task Prioritization: Many methods involve prioritizing tasks based on their importance and urgency. Such methods help focus on the most critical tasks and minimize distractions.[2]
  3. Decision-Making Methods: Making informed decisions is a key part of personal planning. Various decision-making methods, such as SWOT analysis, the "Pros and Cons" method, and data-based decision-making methods, are used to choose the best alternatives.
  4. Modern Applications and Tools: In the modern world, there are numerous applications and software tools for personal planning, including task management apps, event planning, and time tracking tools.[3]
  5. Traditional Methods and Psychological Approaches: Methods like the Eisenhower Matrix and Maslow's Hierarchy of Needs use psychological and organizational principles for personal planning.[4]

Analyzing existing methods and approaches to personal planning helps understand their strengths and weaknesses and identifies areas where they can be improved. This provides a foundation for developing new methods based on neural networks that can overcome the limitations of existing approaches.

3.2 Application of Neural Networks in Operational and Long-Term Planning

Neural networks provide a powerful tool for solving various tasks in operational and long-term personal planning. In this section, we will explore the specific tasks that can be addressed using neural networks and the advantages they offer:

  1. Forecasting Task Priorities: Neural networks can be trained to analyze historical data about tasks and events to predict their priority in the future. This allows users to focus on the most important tasks.
  2. Optimizing Time Allocation: Neural networks can assist in optimizing the allocation of time between different tasks and events, taking into account their importance and urgency. This enables efficient use of available time.
  3. Automatic Decision Recommendations: Neural networks can offer recommendations and solutions based on data analysis and user preferences. For example, they can suggest the optimal time for task completion or even automatically create action plans.
  4. Adaptation to Changes: Neural networks can adapt to changing conditions and predict the impact of future changes on personal planning. This allows for more flexible responses to unexpected events.
  5. Personalized Approach: Neural networks can consider individual characteristics and user preferences, creating plans and recommendations that best align with their needs.

The application of neural networks in personal planning promises to enhance the efficiency and adaptability of this process. They can analyze vast amounts of data, discover patterns, and provide more informed recommendations, making personal planning more intelligent and effective.

3.3 Development of New Methods Based on Neural Networks for Personal Planning

To optimize personal planning and achieve goals, we are developing new methods based on the use of neural networks. The development process includes the following stages:

  1. Selection of Neural Network Types: One of the first steps is to choose appropriate types of neural networks to be applied in personal planning. This may include recurrent neural networks (RNN), convolutional neural networks (CNN), or combinations of different architectures.
  2. Creation of Training Data: Training neural networks requires the creation of a dataset containing information about tasks, events, priorities, and other parameters needed for planning. This stage involves data collection, structuring, and annotation.
  3. Training of Neural Networks: Neural networks are trained based on the created dataset. This process includes tuning network parameters, selecting loss functions, and optimization methods.
  4. Development of Algorithms for Personal Planning: Based on trained neural networks, algorithms and methods are developed that can provide solutions and recommendations for personal planning. These methods take into account priorities, deadlines, and individual user preferences.
  5. Interface and Visualization: Creating a user-friendly interface that allows users to interact with the new planning methods. It is essential for the methods to be easily accessible and understandable.
  6. Testing and Optimization: After the development of methods, testing and optimization are conducted. This includes result analysis, error identification, and performance improvement.

The methods developed based on neural networks promise to be more accurate and adaptable to individual user needs. This will enhance the quality of personal planning and increase the efficiency of achieving set tasks and goals.

4. Algorithm for Personal Operational Planning

  1. Data Preprocessing.

Before starting to work with data, preprocessing is necessary. The following steps should be taken with the collected data on student tasks and plans:

  1. Creation of a Convolutional Neural Network.

Let's start by examining the operation of a convolutional neural network, as shown in Figure 1.

Operation Principle

Figure 1 - Operation Principle of a Convolutional Neural Network

It is necessary to use a convolutional neural network for classifying student tasks. The model consists of the following layers:

  1. Model Training.

To train the model, it is necessary to prepare the data. We'll use a dataset containing information about tasks and students' progress in the learning process. For each student in the dataset, the following characteristics are provided:

We consider the task of planning a student's personal affairs as a classification task, where there is a class label depending on whether all tasks were completed on time or not. The class label will take the value 1 if all tasks were completed on time and 0 otherwise.

For model training, a convolutional neural network with multiple convolutional layers[6], pooling layers[7], and fully connected layers is used. The network architecture will look as follows:

For model training, backpropagation with the Adam optimizer is used. Binary cross-entropy is used as the error function[8].

Results

To test the model, we'll use a dataset that was not used during model training. The data needs to be loaded into the model, and the predicted values should be compared with the actual class labels.

For assessing the model's quality, accuracy, recall, and F1-score metrics are used[9]. Below, in Figure 2, there's an example code snippet in the C# language demonstrating the model's training and testing.

 

C# Code for Model Training and Testing

Figure 2 – C# Code for Model Training and Testing

 

The results displayed on the screen after running the training and testing code are shown in Figure 3.

 

Results of Model Training and Testing

Figure 3 – Results of Model Training and Testing

 

Here, "Test Loss" is the loss value on the test dataset, and "Test Accuracy" is the model's accuracy on the test dataset. In this case, the accuracy is 0.945, indicating that the model correctly classified 94.5% of the test examples.

For training and testing, task lists that need to be completed throughout the day and their priorities were used as input data. Each task was a text string, and its priority was a number between 0 and 1. All data is stored in the "data.csv" file in CSV (Comma-Separated Values) format, where each row corresponds to one task. An example of the file "data.csv" contents is shown in Figure 4.

 

Example

Figure 4 – Example Contents of the "data.csv" File

Here, each row corresponds to one task, consisting of two fields separated by a comma: "text" - the textual task description, and "priority" - its priority represented as a number between 0 and 1.

Conclusions

In this study, the applicability of convolutional neural networks for the operational planning of a student's personal affairs was explored. A detailed algorithm for solving this problem was described, which included the use of convolutional neural networks. Additionally, an overview of existing approaches to solving planning problems was conducted, and the most suitable approach for the task was chosen.

Researching the applicability of convolutional neural networks for the operational planning of a student's personal affairs represents a significant step in applying modern machine learning methods to everyday tasks. An algorithm based on convolutional neural networks presents an innovative approach to solving this issue.

This method not only provides a new level of accuracy and efficiency in solving planning tasks but also ensures a more flexible and adaptive system for managing a student's personal affairs. By using data and training a neural network based on this data, it is possible to create a personalized planning system that takes into account the individual preferences and lifestyles of each student.

List of sources

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